{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.68</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.92</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.73</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.75</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.81</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.76</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.85</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.79</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.64</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.79</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.66</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.67</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.66</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.75</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.64</td>\n",
       "      <td>0.47</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.70</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.74</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.55</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.63</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.78</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.66</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.79</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.78</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.81</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.78</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.58</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.75</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.66</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.74</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.66</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.67</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.61</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.71</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.70</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.76</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.68</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.82</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.79</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0.76</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0.67</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0.62</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0.74</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.51</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0.93</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.73</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0.78</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.59</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>0.69</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>0.57</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>0.79</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       A     B     C\n",
       "0   0.68  0.64  0.83\n",
       "1   0.92  0.89  0.97\n",
       "2   0.73  0.74  0.90\n",
       "3   0.75  0.64  0.79\n",
       "4   0.72  0.75  0.85\n",
       "5   0.81  0.86  0.95\n",
       "6   0.76  0.89  0.97\n",
       "7   0.85  0.90  0.96\n",
       "8   0.79  0.72  0.85\n",
       "9   0.64  0.54  0.96\n",
       "10  0.79  0.80  0.86\n",
       "11  0.66  0.85  0.87\n",
       "12  0.72  0.88  0.87\n",
       "13  0.67  0.58  0.82\n",
       "14  0.66  0.77  0.88\n",
       "15  0.75  0.69  0.94\n",
       "16  0.64  0.47  1.00\n",
       "17  0.70  0.58  0.74\n",
       "18  0.74  0.53  0.92\n",
       "19  0.55  0.65  0.91\n",
       "20  0.63  0.46  0.89\n",
       "21  0.78  0.73  0.93\n",
       "22  0.66  0.61  0.92\n",
       "23  0.79  0.79  0.85\n",
       "24  0.78  0.62  0.90\n",
       "25  0.81  0.82  0.97\n",
       "26  0.78  0.75  0.91\n",
       "27  0.58  0.62  0.86\n",
       "28  0.75  0.63  0.96\n",
       "29  0.66  0.74  0.94\n",
       "30  0.74  0.90  0.97\n",
       "31  0.66  0.58  0.82\n",
       "32  0.67  0.68  0.71\n",
       "33  0.61  0.68  0.89\n",
       "34  0.71  0.77  0.84\n",
       "35  0.70  0.44  0.91\n",
       "36  0.76  0.73  0.94\n",
       "37  0.68  0.57  0.89\n",
       "38  0.82  0.77  0.80\n",
       "39  0.72  0.83  0.90\n",
       "40  0.79  0.81  0.90\n",
       "41  0.76  0.82  0.95\n",
       "42  0.67  0.78  0.90\n",
       "43  0.62  0.31  0.90\n",
       "44  0.74  0.81  0.91\n",
       "45  0.51  0.54  0.80\n",
       "46  0.93  0.76  0.98\n",
       "47  0.73  0.74  0.93\n",
       "48  0.78  0.52  0.90\n",
       "49  0.59  0.68  0.86\n",
       "50  0.69  0.85  0.69\n",
       "51  0.57  0.61  0.83\n",
       "52  0.79  0.62  0.85\n",
       "53  0.72  0.70  0.89"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('个体达成度.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='idx', ylabel='B'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[:, 'idx'] = df.index\n",
    "\n",
    "df.plot('idx','B',kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "def plotScatter(col: str, linePos: float):\n",
    "    colors = ['red' if v < linePos else 'purple' for v in df[col]]\n",
    "    plt.scatter(df.index, df[col], c=colors)\n",
    "    plt.plot([linePos for _ in df[col]],color='c')\n",
    "\n",
    "\n",
    "plotScatter('A', 0.7)\n"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "a9661a0f83c933cf25e0dd40af67627cb20747a37143c65b775945a664681eb4"
  },
  "kernelspec": {
   "display_name": "Python 3.8.5 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
